on_boarding.md

graph LR
    Fuzzing_Core_Orchestration["Fuzzing Core & Orchestration"]
    Instrumentation_Engine["Instrumentation Engine"]
    Runtime_Feedback_Hooks["Runtime Feedback & Hooks"]
    Native_Fuzzing_Input_Generation["Native Fuzzing & Input Generation"]
    Fuzz_Target_Examples["Fuzz Target & Examples"]
    Benchmarking_Analysis["Benchmarking & Analysis"]
    Fuzzing_Core_Orchestration -- "activates and provides modules for instrumentation" --> Instrumentation_Engine
    Instrumentation_Engine -- "injects hooks and configures callbacks" --> Runtime_Feedback_Hooks
    Fuzzing_Core_Orchestration -- "requests inputs and leverages native capabilities" --> Native_Fuzzing_Input_Generation
    Native_Fuzzing_Input_Generation -- "supplies mutated inputs and provides native fuzzing interface" --> Fuzzing_Core_Orchestration
    Fuzzing_Core_Orchestration -- "loads and executes and defines fuzz entry points" --> Fuzz_Target_Examples
    Fuzz_Target_Examples -- "triggers runtime events and invokes instrumented code" --> Runtime_Feedback_Hooks
    Runtime_Feedback_Hooks -- "sends coverage feedback and reports execution data" --> Fuzzing_Core_Orchestration
    Fuzzing_Core_Orchestration -- "provides execution data and sends performance metrics" --> Benchmarking_Analysis
    click Fuzzing_Core_Orchestration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/atheris/Fuzzing_Core_Orchestration.md" "Details"
    click Instrumentation_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/atheris/Instrumentation_Engine.md" "Details"
    click Runtime_Feedback_Hooks href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/atheris/Runtime_Feedback_Hooks.md" "Details"
    click Native_Fuzzing_Input_Generation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/atheris/Native_Fuzzing_Input_Generation.md" "Details"

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Details

The Atheris fuzzing framework operates around a central Fuzzing Core & Orchestration component, which manages the overall fuzzing campaign. This core component initiates the fuzzing loop, orchestrates input generation, and coordinates with other modules. It interacts with the Instrumentation Engine to dynamically inject tracing and coverage hooks into the Python bytecode of the target application. During execution, the instrumented code triggers callbacks managed by the Runtime Feedback & Hooks component, which collects coverage information and reports execution data back to the Fuzzing Core. For input generation, the Fuzzing Core leverages the Native Fuzzing & Input Generation component, which integrates with native fuzzing libraries like libFuzzer and libprotobuf-mutator to generate and mutate inputs based on feedback. Users define their code under test within the Fuzz Target & Examples component, which provides the entry points for fuzzing and interacts with the Runtime Feedback & Hooks during execution. Finally, the Benchmarking & Analysis component receives execution data and performance metrics from the Fuzzing Core to evaluate the efficiency of fuzzing campaigns. This architecture ensures a clear data flow from input generation and code instrumentation through execution, feedback collection, and performance analysis, making it suitable for both detailed documentation and visual diagram generation.

Fuzzing Core & Orchestration [Expand]

The central control unit managing the fuzzing loop, campaign initialization, and coordination between all other components. It orchestrates input generation, target execution, and feedback processing.

Related Classes/Methods:

• atheris_no_libfuzzer.py
• src/__init__.py

Instrumentation Engine [Expand]

Responsible for dynamically modifying Python bytecode to inject tracing and coverage hooks, and intercepting module imports to ensure target code is instrumented before execution.

Related Classes/Methods:

• src/instrument_bytecode.py
• src.version_dependent.py
• src/import_hook.py

Runtime Feedback & Hooks [Expand]

Manages the C/C++ callbacks and Python functions invoked by instrumented code, collecting coverage information and interacting with the underlying fuzzer.

Related Classes/Methods:

• src/function_hooks.py

Native Fuzzing & Input Generation [Expand]

Handles the integration of native fuzzing libraries (like libFuzzer and libprotobuf-mutator) and is responsible for generating and mutating inputs based on feedback.

Related Classes/Methods:

• contrib/libprotobuf_mutator/atheris_libprotobuf_mutator/mutator.cc
• contrib/libprotobuf_mutator/atheris_libprotobuf_mutator/helpers.py

Fuzz Target & Examples

Provides the framework and demonstrative implementations for users to define their specific code under test and integrate it with Atheris.

Related Classes/Methods:

• example_fuzzers/idna_fuzzer/idna_acceptance_fuzzer.py
• example_fuzzers/json_fuzzer/json_differential_fuzzer.py

Benchmarking & Analysis

Focuses on measuring the performance of fuzzing campaigns and analyzing execution statistics to evaluate fuzzer efficiency.

Related Classes/Methods:

• src/benchmark/executions.py

FAQ